Lipid Metabolism classification of gliomas

Background Lipid metabolic reprogramming represents a fundamental oncogenic mechanism. However, clinical relevance of lipid metabolism (LM) alterations in gliomas remain to be fully elucidated. Methods LM classification and following correlative analyses were conducted using glioma bulk RNA-seq datasets downloaded from The Cancer Genome Atlas (TCGA). Radiomic models were constructed and validated using MRI datasets from The Cancer Imaging Archive and our glioma cohort. Immunohistochemical (IHC) staining was used to measure protein expression of key gene in glioma tissues. Single-cell RNA-seq datasets from the GBMap dataset were utilized to characterize distribution and functional relevance of key gene in tumor microenvironment (TME) of gliomas. Results Consensus clustering of LM pathways revealed three subtypes that were respectively dominated by steroid metabolism (ST-type), triglyceride metabolism (TC-type), and sphingolipid metabolism (SP-type). The SP-type independently correlated with worse outcomes, and exhibited increased activity in pathways related to aggressive phenotypes and radiotherapy resistance. Radiomic features were capable of accurate identification of SP-type gliomas, thus providing a non-invasive approach to predict outcomes. Genes GLA, GBL1, and HSD3B7 emerged as signature genes for the SP-type. Higher expression level of HSD3B7 associtaed with poor prognosis and exhibited functional relevance to multiple signaling pathways within diverse TME cellular components that eventually promote glioma progression. Conclusion Gliomas exhibited marked heterogeneity in LM and can be classified into three subtypes, with the SP-type exhibiting the most aggressive clinical behavior.